| import functools |
| import torch |
| import torch.nn as nn |
| from networks.base_model import BaseModel, init_weights |
| import sys |
| from models import get_model |
|
|
| class Trainer(BaseModel): |
| def name(self): |
| return 'Trainer' |
|
|
| def __init__(self, opt): |
| super(Trainer, self).__init__(opt) |
| self.opt = opt |
| self.model = get_model(opt.arch) |
| torch.nn.init.normal_(self.model.fc.weight.data, 0.0, opt.init_gain) |
|
|
| if opt.fix_backbone: |
| params = [] |
| for name, p in self.model.named_parameters(): |
| if name=="fc.weight" or name=="fc.bias": |
| params.append(p) |
| else: |
| p.requires_grad = False |
| else: |
| print("Your backbone is not fixed. Are you sure you want to proceed? If this is a mistake, enable the --fix_backbone command during training and rerun") |
| import time |
| time.sleep(3) |
| params = self.model.parameters() |
|
|
| |
|
|
| if opt.optim == 'adam': |
| self.optimizer = torch.optim.AdamW(params, lr=opt.lr, betas=(opt.beta1, 0.999), weight_decay=opt.weight_decay) |
| elif opt.optim == 'sgd': |
| self.optimizer = torch.optim.SGD(params, lr=opt.lr, momentum=0.0, weight_decay=opt.weight_decay) |
| else: |
| raise ValueError("optim should be [adam, sgd]") |
|
|
| self.loss_fn = nn.BCEWithLogitsLoss() |
|
|
| self.model.to(opt.gpu_ids[0]) |
|
|
|
|
| def adjust_learning_rate(self, min_lr=1e-6): |
| for param_group in self.optimizer.param_groups: |
| param_group['lr'] /= 10. |
| if param_group['lr'] < min_lr: |
| return False |
| return True |
|
|
|
|
| def set_input(self, input): |
| self.input = input[0].to(self.device) |
| self.label = input[1].to(self.device).float() |
|
|
|
|
| def forward(self): |
| self.output = self.model(self.input) |
| self.output = self.output.view(-1).unsqueeze(1) |
|
|
|
|
| def get_loss(self): |
| return self.loss_fn(self.output.squeeze(1), self.label) |
|
|
| def optimize_parameters(self): |
| self.forward() |
| self.loss = self.loss_fn(self.output.squeeze(1), self.label) |
| self.optimizer.zero_grad() |
| self.loss.backward() |
| self.optimizer.step() |
|
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|
